Generating pedestrian maps of disaster areas through ad-hoc deployment of computing resources across a DTN

• Published in: Computer communications Vol. 100; pp. 129 - 142
• Main Authors: Trono, Edgar Marko, Fujimoto, Manato, Suwa, Hirohiko, Arakawa, Yutaka, Yasumoto, Keiichi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2017
• Subjects: Data ferries; Delay tolerant networks; Disaster area mapping; Load balancing; Data ferries; Disaster area mapping; Load balancing; Delay tolerant networks
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2016.12.003

Generating pedestrian maps of disaster areas is an important part of response operations. Maps aid responders in decision-making and show routes that lead evacuees to refuges. However, disasters can damage communication infrastructures, rendering Cloud-based mapping services inaccessible. Responders resort to paper maps, which are difficult to share and cannot recommend routes. In this study, we present a digital pedestrian map generation system for disasters. To realize the system, we addressed these challenges: (1) how to collect the required data and generate the map without Cloud-based computing resources, (2) how to share messages within the system without continuous, end-to-end networks, and (3) how to balance the load of map inference tasks. For (1), GPS traces are collected by responders exploring the area. Then, collected data are sent to Computing Nodes: commodity workstations that are deployed in the disaster area, for processing. For (2), the system establishes a Delay-Tolerant Network that uses Epidemic Routing to communicate across shorter-ranges and uses response vehicles as data ferries to communicate across longer-ranges. For (3), we propose a load balancing heuristic, which uses ferry route timetables and statistical information about the load of Computing Nodes to determine how to offload map inference tasks. We evaluate our system through experiments and simulations and show that it decreases the time needed to generate and deliver pieces of the map by approximately two hours in an extreme case with large quantities of data have to be processed.